{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9e01488e-1767-470e-9dbc-3d198dc1b1d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ee52b555-8848-497e-9c01-a879001adca4",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>语音通话整体满意度</th>\n",
       "      <th>网络覆盖与信号强度</th>\n",
       "      <th>语音通话清晰度</th>\n",
       "      <th>语音通话稳定性</th>\n",
       "      <th>当月ARPU</th>\n",
       "      <th>前3月MOU</th>\n",
       "      <th>脱网次数</th>\n",
       "      <th>套外流量费（元）</th>\n",
       "      <th>套外流量（MB）</th>\n",
       "      <th>未接通掉话次数</th>\n",
       "      <th>...</th>\n",
       "      <th>居民小区_-1</th>\n",
       "      <th>居民小区_1</th>\n",
       "      <th>是否4G网络客户（本地剔除物联网）_否</th>\n",
       "      <th>是否4G网络客户（本地剔除物联网）_是</th>\n",
       "      <th>高铁_-1</th>\n",
       "      <th>高铁_7</th>\n",
       "      <th>商业街_-1</th>\n",
       "      <th>商业街_4</th>\n",
       "      <th>是否关怀用户_否</th>\n",
       "      <th>是否关怀用户_是</th>\n",
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       "      <td>10</td>\n",
       "      <td>6</td>\n",
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       "      <td>6</td>\n",
       "      <td>347.32</td>\n",
       "      <td>480</td>\n",
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       "      <td>250.0</td>\n",
       "      <td>1816.45</td>\n",
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       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>111.40</td>\n",
       "      <td>480</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
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       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>48.00</td>\n",
       "      <td>413</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "      <th>3</th>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>49.00</td>\n",
       "      <td>301</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>138.10</td>\n",
       "      <td>1193</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 403 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   语音通话整体满意度  网络覆盖与信号强度  语音通话清晰度  语音通话稳定性  当月ARPU  前3月MOU  脱网次数  套外流量费（元）  \\\n",
       "0         10          6        6        6  347.32     480     0     250.0   \n",
       "1          2          1        1        1  111.40     480     0       0.0   \n",
       "2         10          7        7        7   48.00     413     0       0.0   \n",
       "3          6          7        7        6   49.00     301     0       0.0   \n",
       "4          5          5        4        3  138.10    1193     0       0.0   \n",
       "\n",
       "   套外流量（MB）  未接通掉话次数  ...  居民小区_-1  居民小区_1  是否4G网络客户（本地剔除物联网）_否  \\\n",
       "0   1816.45        0  ...        1       0                    0   \n",
       "1      0.00        0  ...        0       1                    0   \n",
       "2      0.00        0  ...        1       0                    0   \n",
       "3      0.00        0  ...        0       1                    0   \n",
       "4      0.00        0  ...        1       0                    0   \n",
       "\n",
       "   是否4G网络客户（本地剔除物联网）_是  高铁_-1  高铁_7  商业街_-1  商业街_4  是否关怀用户_否  是否关怀用户_是  \n",
       "0                    1      1     0       1      0         1         0  \n",
       "1                    1      1     0       0      1         1         0  \n",
       "2                    1      1     0       1      0         1         0  \n",
       "3                    1      1     0       1      0         1         0  \n",
       "4                    1      0     1       1      0         1         0  \n",
       "\n",
       "[5 rows x 403 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "traindata=pd.read_csv('./ans1/traindata_clean_附件1.csv',encoding='gbk')\n",
    "traindata.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c95f1dca-fd78-4224-9416-8ae7f854c0cc",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>用户id</th>\n",
       "      <th>当月ARPU</th>\n",
       "      <th>前3月MOU</th>\n",
       "      <th>脱网次数</th>\n",
       "      <th>套外流量费（元）</th>\n",
       "      <th>套外流量（MB）</th>\n",
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       "      <th>GPRS-国内漫游-流量（KB）</th>\n",
       "      <th>...</th>\n",
       "      <th>居民小区_-1</th>\n",
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       "      <th>是否4G网络客户（本地剔除物联网）_是</th>\n",
       "      <th>高铁_-1</th>\n",
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       "      <td>13</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4</td>\n",
       "      <td>662</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
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       "      <td>56.00</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 186 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   用户id  当月ARPU  前3月MOU  脱网次数  套外流量费（元）  套外流量（MB）  未接通掉话次数  语音通话-时长（分钟）  \\\n",
       "0     1   45.37     139     1       2.9      9.55        0          161   \n",
       "1     2   60.00     102     0       0.0      0.00        1          270   \n",
       "2     3   89.00      80    13       0.0      0.00        4          662   \n",
       "3     4   70.00     333     8       0.0      0.00        2          645   \n",
       "4     5   56.00     258    22       0.0      0.00        2          496   \n",
       "\n",
       "   省际漫游-时长（分钟）  GPRS-国内漫游-流量（KB）  ...  居民小区_-1  居民小区_1  是否4G网络客户（本地剔除物联网）_否  \\\n",
       "0            0                 0  ...        1       0                    0   \n",
       "1            0                 0  ...        0       1                    0   \n",
       "2            0                 0  ...        1       0                    0   \n",
       "3            0                 0  ...        0       1                    0   \n",
       "4            0                 0  ...        0       1                    0   \n",
       "\n",
       "   是否4G网络客户（本地剔除物联网）_是  高铁_-1  高铁_7  商业街_-1  商业街_4  是否关怀用户_否  是否关怀用户_是  \n",
       "0                    1      1     0       1      0         1         0  \n",
       "1                    1      1     0       1      0         1         0  \n",
       "2                    1      1     0       1      0         1         0  \n",
       "3                    1      1     0       1      0         1         0  \n",
       "4                    1      1     0       1      0         1         0  \n",
       "\n",
       "[5 rows x 186 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "testdata=pd.read_csv('./ans1/testdata_clean_附件3.csv',encoding='gbk')\n",
    "testdata.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f6e97594-36f5-4e9e-8d00-cfa7ecc63096",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['有信号无法拨通_2',\n",
       " '是否关怀用户_是',\n",
       " '手机没有信号_-1',\n",
       " '其他，请注明.1_-1',\n",
       " '语音通话-时长（分钟）',\n",
       " '通话过程中一方听不见_-1',\n",
       " '农村_6',\n",
       " '是否关怀用户_否',\n",
       " '客户星级标识_金卡',\n",
       " '是否遇到过网络问题_2',\n",
       " '套外流量费（元）',\n",
       " '高铁_-1',\n",
       " '外省流量占比',\n",
       " '客户星级标识_准星',\n",
       " '地铁_-1',\n",
       " '其他，请注明_98',\n",
       " '当月ARPU',\n",
       " '高校_3',\n",
       " '通话过程中突然中断_-1',\n",
       " '是否4G网络客户（本地剔除物联网）_否',\n",
       " '客户星级标识_钻石卡',\n",
       " 'mos质差次数',\n",
       " '串线_-1',\n",
       " '脱网次数',\n",
       " '商业街_4',\n",
       " '省际漫游-时长（分钟）',\n",
       " '4\\\\5G用户_5G',\n",
       " '是否5G网络客户_否',\n",
       " '外省语音占比_0.0',\n",
       " '客户星级标识_一星',\n",
       " '是否5G网络客户_是',\n",
       " '农村_-1',\n",
       " '其他，请注明_-1',\n",
       " '通话中有杂音、听不清、断断续续_4',\n",
       " '是否遇到过网络问题_1',\n",
       " '居民小区_1',\n",
       " '4\\\\5G用户_2G',\n",
       " 'GPRS-国内漫游-流量（KB）',\n",
       " '串线_5',\n",
       " '通话过程中一方听不见_6',\n",
       " '当月MOU',\n",
       " '是否4G网络客户（本地剔除物联网）_是',\n",
       " '前3月ARPU',\n",
       " '客户星级标识_二星',\n",
       " '居民小区_-1',\n",
       " '4\\\\5G用户_4G',\n",
       " '手机没有信号_1',\n",
       " '商业街_-1',\n",
       " '通话中有杂音、听不清、断断续续_-1',\n",
       " '高铁_7',\n",
       " '客户星级标识_未评级',\n",
       " '客户星级标识_银卡',\n",
       " '前3月MOU',\n",
       " '地铁_5',\n",
       " '通话过程中突然中断_3',\n",
       " '办公室_-1',\n",
       " '客户星级标识_白金卡',\n",
       " '其他，请注明.1_98',\n",
       " '未接通掉话次数',\n",
       " '套外流量（MB）',\n",
       " '有信号无法拨通_-1',\n",
       " '客户星级标识_三星',\n",
       " '办公室_2',\n",
       " '高校_-1']"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "setcols=list(set(traindata.columns).intersection(set(testdata.columns)))  \n",
    "setcols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "207b12e1-ba5c-44db-8524-bb92b127cf84",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.tree import plot_tree\n",
    "\n",
    "from sklearn import tree\n",
    "\n",
    "from sklearn.metrics import classification_report\n",
    "\n",
    "import numpy as np\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "617f5520-d13b-4cbc-8595-a198e0c32d4a",
   "metadata": {},
   "outputs": [],
   "source": [
    "Y1=traindata[traindata.columns[0]]\n",
    "Y2=traindata[traindata.columns[1]]\n",
    "Y3=traindata[traindata.columns[2]]\n",
    "Y4=traindata[traindata.columns[3]]\n",
    "X=traindata[setcols]\n",
    "test_X=testdata[setcols]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "33d98cab-8147-4d9a-9cd1-d09237254d7a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "全部数据训练效果\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           1       1.00      1.00      1.00       209\n",
      "           2       1.00      1.00      1.00        42\n",
      "           3       1.00      1.00      1.00        71\n",
      "           4       1.00      1.00      1.00        55\n",
      "           5       1.00      1.00      1.00       172\n",
      "           6       1.00      1.00      1.00       182\n",
      "           7       1.00      1.00      1.00       214\n",
      "           8       1.00      1.00      1.00       567\n",
      "           9       1.00      1.00      1.00       764\n",
      "          10       1.00      1.00      1.00      3157\n",
      "\n",
      "    accuracy                           1.00      5433\n",
      "   macro avg       1.00      1.00      1.00      5433\n",
      "weighted avg       1.00      1.00      1.00      5433\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#选择决策树模型为：entropy。\n",
    "YY=Y1\n",
    "\n",
    "DT=DecisionTreeClassifier( criterion='entropy')\n",
    "\n",
    "see1=DT.fit(X,YY)\n",
    "\n",
    "y_pred=DT.predict(X)\n",
    "print('全部数据训练效果')\n",
    "print(classification_report(y_pred,YY))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "87680994-a16d-4aaa-840c-8a69dcbf07d7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "from matplotlib import pyplot as plt\n",
    "import seaborn as sns\n",
    "dinglei=['地铁','是否遇到过网络问题','通话过程中突然中断', '是否5G网络客户', '外省语音占比', '办公室','手机没有信号','其他，请注明.1', '串线', '农村','高校', '客户星级标识',\n",
    " '通话过程中一方听不见',\n",
    " '其他，请注明','4\\\\5G用户', '通话中有杂音、听不清、断断续续', '有信号无法拨通',\n",
    " '居民小区',\n",
    " '是否4G网络客户（本地剔除物联网）',\n",
    " '高铁',\n",
    " '商业街', '是否关怀用户']\n",
    "dingliang=['语音通话整体满意度', '网络覆盖与信号强度', '语音通话清晰度', '语音通话稳定性','当月ARPU','前3月MOU', '脱网次数', '套外流量费（元）', '套外流量（MB）', '未接通掉话次数', '语音通话-时长（分钟）', '省际漫游-时长（分钟）', 'GPRS-国内漫游-流量（KB）', '当月MOU', 'mos质差次数',\n",
    " '前3月ARPU', '外省流量占比']\n",
    "# 重要性\n",
    "features_import = pd.DataFrame(X.columns, columns=['feature'])\n",
    "features_import['importance'] = DT.feature_importances_  # 默认按照gini计算特征重要性\n",
    "allcol=dinglei+dingliang\n",
    "def getcol(x):\n",
    "    for i in allcol:\n",
    "        if i in x:\n",
    "            return i\n",
    "\n",
    "features_import['feature']=features_import['feature'].apply(lambda x:getcol(x))\n",
    "features_import=features_import.groupby('feature').sum().reset_index(drop=False)\n",
    "features_import.sort_values('importance', inplace=True)\n",
    "# 绘图\n",
    "from matplotlib import pyplot as plt\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']  # 显示中文黑体\n",
    "# plt.rcParams['axes.unicode_minus'] = False # 负值显示\n",
    "fig, ax = plt.subplots(figsize=(15, 10))\n",
    "\n",
    "plt.barh(features_import['feature'], features_import['importance'], height=0.7, color='#008792', edgecolor='#005344') # 更多颜色可参见颜色大全\n",
    "plt.xlabel('feature importance') # x 轴\n",
    "plt.ylabel('features') # y轴\n",
    "plt.title('语音通话整体满意度 Feature Importances') # 标题\n",
    "for a,b in zip( features_import['importance'],features_import['feature']): # 添加数字标签\n",
    "    plt.text(a+0.001, b,'%.3f'%float(a)) # a+0.001代表标签位置在柱形图上方0.001处\n",
    "plt.savefig('./ans2/语音通话整体满意度featureimportance')\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "37827f6d-8920-442f-9ed3-33c1a032a13e",
   "metadata": {},
   "outputs": [],
   "source": [
    "dinglei=['地铁','是否遇到过网络问题','通话过程中突然中断', '是否5G网络客户', '外省语音占比', '办公室','手机没有信号','其他，请注明.1', '串线', '农村','高校', '客户星级标识',\n",
    " '通话过程中一方听不见',\n",
    " '其他，请注明','4\\\\5G用户', '通话中有杂音、听不清、断断续续', '有信号无法拨通',\n",
    " '居民小区',\n",
    " '是否4G网络客户（本地剔除物联网）',\n",
    " '高铁',\n",
    " '商业街', '是否关怀用户']\n",
    "dingliang=['语音通话整体满意度', '网络覆盖与信号强度', '语音通话清晰度', '语音通话稳定性','当月ARPU','前3月MOU', '脱网次数', '套外流量费（元）', '套外流量（MB）', '未接通掉话次数', '语音通话-时长（分钟）', '省际漫游-时长（分钟）', 'GPRS-国内漫游-流量（KB）', '当月MOU', 'mos质差次数',\n",
    " '前3月ARPU', '外省流量占比']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "5b338f54-066a-4f0b-84ad-c81b6b9ab34f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 此处所引入的包大部分为下文机器学习算法\n",
    "import pandas as pd\n",
    "from numpy import *\n",
    "import numpy as np\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import learning_curve\n",
    "import xgboost as xgb\n",
    "import lightgbm as lgb\n",
    "from sklearn.metrics import accuracy_score,recall_score,f1_score\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "from sklearn import svm\n",
    "\n",
    "from random import sample\n",
    "from sklearn.model_selection import ShuffleSplit\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "from sklearn.model_selection import train_test_split\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "33f1581a-8993-4fb9-87d3-7bda0e87fc5e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "!{sys.executable} -m pip install xgboost -i https://pypi.tuna.tsinghua.edu.cn/simple\n",
    "!{sys.executable} -m pip install lightgbm -i https://pypi.tuna.tsinghua.edu.cn/simple"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "59b9fbc2-a820-4b4d-bf50-00a672e4f6aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "tr_x,te_x,tr_y,te_y=train_test_split(X,YY,test_size=0.3,random_state=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "5279a98f-e186-4b35-a30c-1bd8b4fef16c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "神经网络:\n",
      "训练集准确度:0.520\n",
      "测试集准确度:0.501\n",
      "平均绝对误差: 1.5901840490797545\n",
      "ACC 0.5006134969325153\n",
      "REC 0.5006134969325153\n",
      "F-score 0.5006134969325153\n",
      "\n",
      "逻辑回归:\n",
      "训练集准确度:0.586\n",
      "测试集准确度:0.569\n",
      "平均绝对误差: 1.328834355828221\n",
      "ACC 0.5687116564417178\n",
      "REC 0.5687116564417178\n",
      "F-score 0.5687116564417178\n",
      "\n",
      "决策树:\n",
      "训练集准确度:1.000\n",
      "测试集准确度:0.438\n",
      "平均绝对误差: 1.5644171779141105\n",
      "ACC 0.43803680981595094\n",
      "REC 0.43803680981595094\n",
      "F-score 0.43803680981595094\n",
      "\n",
      "随机森林:\n",
      "训练集准确度:0.944\n",
      "测试集准确度:0.575\n",
      "平均绝对误差: 1.2460122699386502\n",
      "ACC 0.5748466257668712\n",
      "REC 0.5748466257668712\n",
      "F-score 0.5748466257668712\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "\n",
    "model=MLPClassifier(hidden_layer_sizes=10,max_iter=1000).fit(tr_x,tr_y)\n",
    "print(\"神经网络:\")\n",
    "print(\"训练集准确度:{:.3f}\".format(model.score(tr_x,tr_y)))\n",
    "print(\"测试集准确度:{:.3f}\".format(model.score(te_x,te_y)))\n",
    "y_pred = model.predict(te_x)\n",
    "print(\"平均绝对误差:\",mean_absolute_error(te_y, y_pred))\n",
    "# 准确率，召回率，F-score评价\n",
    "print(\"ACC\",accuracy_score(te_y,y_pred))\n",
    "print(\"REC\",recall_score(te_y,y_pred,average=\"micro\"))\n",
    "print(\"F-score\",f1_score(te_y,y_pred,average=\"micro\"))\n",
    "\n",
    "\n",
    "print(\"\\n逻辑回归:\")\n",
    "logreg = LogisticRegression(C=1,solver='liblinear',multi_class ='auto')\n",
    "logreg.fit(tr_x, tr_y)\n",
    "score = logreg.score(tr_x, tr_y)\n",
    "score2 = logreg.score(te_x, te_y)\n",
    "print(\"训练集准确度:{:.3f}\".format(logreg.score(tr_x,tr_y)))\n",
    "print(\"测试集准确度:{:.3f}\".format(logreg.score(te_x,te_y)))\n",
    "y_pred = logreg.predict(te_x)\n",
    "print(\"平均绝对误差:\",mean_absolute_error(te_y, y_pred))\n",
    "print(\"ACC\",accuracy_score(te_y,y_pred))\n",
    "print(\"REC\",recall_score(te_y,y_pred,average=\"micro\"))\n",
    "print(\"F-score\",f1_score(te_y,y_pred,average=\"micro\"))\n",
    "\n",
    "\n",
    "print(\"\\n决策树:\")\n",
    "tree=DecisionTreeClassifier(max_depth=50,random_state=0)\n",
    "tree.fit(tr_x,tr_y)\n",
    "y_pred = tree.predict(te_x)\n",
    "print(\"训练集准确度:{:.3f}\".format(tree.score(tr_x,tr_y)))\n",
    "print(\"测试集准确度:{:.3f}\".format(tree.score(te_x,te_y)))\n",
    "print(\"平均绝对误差:\",mean_absolute_error(te_y, y_pred))\n",
    "print(\"ACC\",accuracy_score(te_y,y_pred))\n",
    "print(\"REC\",recall_score(te_y,y_pred,average=\"micro\"))\n",
    "print(\"F-score\",f1_score(te_y,y_pred,average=\"micro\"))\n",
    "\n",
    "print(\"\\n随机森林:\")\n",
    "rf=RandomForestClassifier(max_depth=20,n_estimators=1000,random_state=0)\n",
    "rf.fit(tr_x,tr_y)\n",
    "print(\"训练集准确度:{:.3f}\".format(rf.score(tr_x,tr_y)))\n",
    "print(\"测试集准确度:{:.3f}\".format(rf.score(te_x,te_y)))\n",
    "y_pred = rf.predict(te_x)\n",
    "print(\"平均绝对误差:\",mean_absolute_error(te_y, y_pred))\n",
    "print(\"ACC\",accuracy_score(te_y,y_pred))\n",
    "print(\"REC\",recall_score(te_y,y_pred,average=\"micro\"))\n",
    "print(\"F-score\",f1_score(te_y,y_pred,average=\"micro\"))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "096141df-82f8-4d97-8602-f59cd5e5fc0e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "LGBM:\n",
      "训练集准确度:0.956\n",
      "测试集准确度:0.554\n",
      "平均绝对误差: 1.2546012269938651\n",
      "ACC 0.5539877300613497\n",
      "REC 0.5539877300613497\n",
      "F-score 0.5539877300613497\n",
      "\n",
      "LGBM:\n",
      "[00:19:16] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "训练集准确度:0.958\n",
      "测试集准确度:0.544\n",
      "平均绝对误差: 1.2834355828220858\n",
      "ACC 0.5435582822085889\n",
      "REC 0.5435582822085889\n",
      "F-score 0.5435582822085889\n"
     ]
    }
   ],
   "source": [
    "\n",
    "print(\"\\nLGBM:\")\n",
    "lgb_model=lgb.LGBMClassifier()\n",
    "lgb_model.fit(tr_x,tr_y)\n",
    "print(\"训练集准确度:{:.3f}\".format(lgb_model.score(tr_x,tr_y)))\n",
    "print(\"测试集准确度:{:.3f}\".format(lgb_model.score(te_x,te_y)))\n",
    "y_pred = lgb_model.predict(te_x)\n",
    "print(\"平均绝对误差:\",mean_absolute_error(te_y, y_pred))\n",
    "print(\"ACC\",accuracy_score(te_y,y_pred))\n",
    "print(\"REC\",recall_score(te_y,y_pred,average=\"micro\"))\n",
    "print(\"F-score\",f1_score(te_y,y_pred,average=\"micro\"))\n",
    "\n",
    "\n",
    "print(\"\\nXGBOOST:\")\n",
    "xgb_model=xgb.XGBClassifier()\n",
    "xgb_model.fit(tr_x,tr_y)\n",
    "print(\"训练集准确度:{:.3f}\".format(xgb_model.score(tr_x,tr_y)))\n",
    "print(\"测试集准确度:{:.3f}\".format(xgb_model.score(te_x,te_y)))\n",
    "y_pred = xgb_model.predict(te_x)\n",
    "print(\"平均绝对误差:\",mean_absolute_error(te_y, y_pred))\n",
    "print(\"ACC\",accuracy_score(te_y,y_pred))\n",
    "print(\"REC\",recall_score(te_y,y_pred,average=\"micro\"))\n",
    "print(\"F-score\",f1_score(te_y,y_pred,average=\"micro\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "c2b5e944-18bd-41f7-be2e-18ed6c3c4a7b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<module 'matplotlib.pyplot' from 'C:\\\\Users\\\\Linling\\\\AppData\\\\Roaming\\\\spsspro\\\\spsspro\\\\lib\\\\python\\\\lib\\\\site-packages\\\\matplotlib\\\\pyplot.py'>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 学习曲线函数\n",
    "\n",
    "def plot_learning_curve(estimator, title, X, y, ylim=None, cv=None,\n",
    "                        n_jobs=1, train_sizes=np.linspace(.1, 1.0, 5)):\n",
    "    plt.figure()\n",
    "    plt.title(title)\n",
    "    if ylim is not None:\n",
    "        plt.ylim(*ylim)\n",
    "    plt.xlabel(\"game num\")\n",
    "    plt.ylabel(\"score\")\n",
    "    train_sizes, train_scores, test_scores = learning_curve(\n",
    "        estimator, X, y, cv=cv, n_jobs=n_jobs, train_sizes=train_sizes)\n",
    "    train_scores_mean = np.mean(train_scores, axis=1)\n",
    "    train_scores_std = np.std(train_scores, axis=1)\n",
    "    test_scores_mean = np.mean(test_scores, axis=1)\n",
    "    test_scores_std = np.std(test_scores, axis=1)\n",
    "    plt.grid()\n",
    "\n",
    "    plt.fill_between(train_sizes, train_scores_mean - train_scores_std,\n",
    "                     train_scores_mean + train_scores_std, alpha=0.1,\n",
    "                     color=\"r\")\n",
    "    plt.fill_between(train_sizes, test_scores_mean - test_scores_std,\n",
    "                     test_scores_mean + test_scores_std, alpha=0.1, color=\"g\")\n",
    "    plt.plot(train_sizes, train_scores_mean, 'o-', color=\"r\",\n",
    "             label=\"Training score\")\n",
    "    plt.plot(train_sizes, test_scores_mean, 'o-', color=\"g\",\n",
    "             label=\"Cross-validation score\")\n",
    "    plt.savefig('./ans2/%s.jpg'%title)\n",
    "    plt.legend(loc=\"best\")\n",
    "    return plt\n",
    "\n",
    "cv = ShuffleSplit(n_splits=5, test_size=0.2, random_state=0)\n",
    "plot_learning_curve(logreg, \"逻辑回归\", tr_x, tr_y, ylim=None, cv=cv, n_jobs=1)\n",
    "plot_learning_curve(tree, \"决策树\", tr_x, tr_y, ylim=None, cv=None, n_jobs=1)\n",
    "plot_learning_curve(rf, \"随机森林\", tr_x, tr_y, ylim=None, cv=None, n_jobs=1)\n",
    "plot_learning_curve(model, \"BP模型\", tr_x, tr_y, ylim=None, cv=None, n_jobs=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "6fe95636-3d0c-412e-9c82-f52ba43269cc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[00:20:59] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[00:20:59] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[00:21:00] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[00:21:00] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[00:21:01] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[00:21:02] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[00:21:02] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[00:21:03] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[00:21:03] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[00:21:04] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[00:21:05] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[00:21:05] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[00:21:06] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[00:21:06] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[00:21:07] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[00:21:08] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[00:21:08] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[00:21:09] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[00:21:09] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[00:21:10] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[00:21:11] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[00:21:11] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[00:21:12] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[00:21:12] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[00:21:13] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<module 'matplotlib.pyplot' from 'C:\\\\Users\\\\Linling\\\\AppData\\\\Roaming\\\\spsspro\\\\spsspro\\\\lib\\\\python\\\\lib\\\\site-packages\\\\matplotlib\\\\pyplot.py'>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "plot_learning_curve(lgb_model, \"LGBM\", tr_x, tr_y, ylim=None, cv=None, n_jobs=1)\n",
    "plot_learning_curve(xgb_model, \"XGBOOST\", tr_x, tr_y, ylim=None, cv=None, n_jobs=1)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "553266db-83b0-4122-b22e-9966000c1797",
   "metadata": {},
   "source": [
    "结果图上可以看出，随着数据量的增加，逻辑回归与BP模型虽然趋近于收敛，但是在训练集和检验集上准确度表现都很差，仅有0.5左右。这预示着存在着很高的偏差，是欠拟合的表现。\n",
    "\n",
    "决策树\\随机森林\\xgboost\\lgbm出现了高方差情形，也就是过拟合的情况。这都预示着我们要找到正确率低原因，并且优化我们的模型。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "8206d90f-e6e9-4b6f-89ba-8244833a2c1b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def GRA_ONE(DataFrame,m=-1):\n",
    "    gray= DataFrame\n",
    "    # 读取为df格式\n",
    "    gray=(gray - gray.min()) / (gray.max() - gray.min())\n",
    "    # 标准化\n",
    "    std = gray.iloc[:, m]  # 为标准要素\n",
    "    ce = gray.iloc[:, 0:]  # 为比较要素\n",
    "    n=ce.shape[0]\n",
    "    m=ce.shape[1]# 计算行列\n",
    "\n",
    "    # 与标准要素比较，相减\n",
    "    a=zeros([m,n])\n",
    "    for i in range(m):\n",
    "        for j in range(n):\n",
    "            a[i,j]=abs(ce.iloc[j,i]-std[j])\n",
    "\n",
    "    # 取出矩阵中最大值与最小值\n",
    "    c=amax(a)\n",
    "    d=amin(a)\n",
    "\n",
    "    # 计算值\n",
    "    result=zeros([m,n])\n",
    "    for i in range(m):\n",
    "        for j in range(n):\n",
    "            result[i,j]=(d+0.5*c)/(a[i,j]+0.5*c)\n",
    "\n",
    "    # 求均值，得到灰色关联值\n",
    "    result2=zeros(m)\n",
    "    for i in range(m):\n",
    "            result2[i]=mean(result[i,:])\n",
    "    RT=pd.DataFrame(result2)\n",
    "    return RT\n",
    "\n",
    "def GRA(DataFrame):\n",
    "    list_columns = [str(s) for s in range(len(DataFrame.columns)) if s not in [None]]\n",
    "    df_local = pd.DataFrame(columns=setcols)\n",
    "    for i in range(len(DataFrame.columns)):\n",
    "        df_local.iloc[:,i] = GRA_ONE(DataFrame,m=i)[0]\n",
    "    return df_local\n",
    "play_score = GRA(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "73cc1051-b9c7-4a8c-be15-7a5928d7541a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>4\\5G用户_5G</th>\n",
       "      <th>通话过程中突然中断_-1</th>\n",
       "      <th>其他，请注明.1_98</th>\n",
       "      <th>4\\5G用户_2G</th>\n",
       "      <th>客户星级标识_准星</th>\n",
       "      <th>客户星级标识_银卡</th>\n",
       "      <th>农村_6</th>\n",
       "      <th>客户星级标识_三星</th>\n",
       "      <th>未接通掉话次数</th>\n",
       "      <th>脱网次数</th>\n",
       "      <th>...</th>\n",
       "      <th>语音通话-时长（分钟）</th>\n",
       "      <th>高铁_-1</th>\n",
       "      <th>套外流量（MB）</th>\n",
       "      <th>客户星级标识_一星</th>\n",
       "      <th>高校_-1</th>\n",
       "      <th>是否5G网络客户_否</th>\n",
       "      <th>有信号无法拨通_2</th>\n",
       "      <th>mos质差次数</th>\n",
       "      <th>外省语音占比_0.0</th>\n",
       "      <th>省际漫游-时长（分钟）</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.628321</td>\n",
       "      <td>0.740720</td>\n",
       "      <td>0.739861</td>\n",
       "      <td>0.743665</td>\n",
       "      <td>0.741702</td>\n",
       "      <td>0.730167</td>\n",
       "      <td>0.615437</td>\n",
       "      <td>0.736468</td>\n",
       "      <td>0.738419</td>\n",
       "      <td>...</td>\n",
       "      <td>0.729502</td>\n",
       "      <td>0.604516</td>\n",
       "      <td>0.742592</td>\n",
       "      <td>0.738143</td>\n",
       "      <td>0.593840</td>\n",
       "      <td>0.339346</td>\n",
       "      <td>0.723050</td>\n",
       "      <td>0.704382</td>\n",
       "      <td>0.606234</td>\n",
       "      <td>0.739304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.628321</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.507700</td>\n",
       "      <td>0.507577</td>\n",
       "      <td>0.506964</td>\n",
       "      <td>0.564145</td>\n",
       "      <td>0.496165</td>\n",
       "      <td>0.717161</td>\n",
       "      <td>0.501141</td>\n",
       "      <td>0.504282</td>\n",
       "      <td>...</td>\n",
       "      <td>0.498788</td>\n",
       "      <td>0.844162</td>\n",
       "      <td>0.505397</td>\n",
       "      <td>0.515430</td>\n",
       "      <td>0.831769</td>\n",
       "      <td>0.704154</td>\n",
       "      <td>0.486594</td>\n",
       "      <td>0.490779</td>\n",
       "      <td>0.798024</td>\n",
       "      <td>0.504007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.740720</td>\n",
       "      <td>0.507700</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.982944</td>\n",
       "      <td>0.983803</td>\n",
       "      <td>0.836800</td>\n",
       "      <td>0.930793</td>\n",
       "      <td>0.605252</td>\n",
       "      <td>0.969890</td>\n",
       "      <td>0.976648</td>\n",
       "      <td>...</td>\n",
       "      <td>0.958225</td>\n",
       "      <td>0.426959</td>\n",
       "      <td>0.982606</td>\n",
       "      <td>0.966501</td>\n",
       "      <td>0.358366</td>\n",
       "      <td>0.592981</td>\n",
       "      <td>0.878766</td>\n",
       "      <td>0.919197</td>\n",
       "      <td>0.407571</td>\n",
       "      <td>0.976645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.739861</td>\n",
       "      <td>0.507577</td>\n",
       "      <td>0.982944</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.992515</td>\n",
       "      <td>0.838395</td>\n",
       "      <td>0.935088</td>\n",
       "      <td>0.607583</td>\n",
       "      <td>0.978635</td>\n",
       "      <td>0.985538</td>\n",
       "      <td>...</td>\n",
       "      <td>0.966709</td>\n",
       "      <td>0.420701</td>\n",
       "      <td>0.991609</td>\n",
       "      <td>0.974968</td>\n",
       "      <td>0.351617</td>\n",
       "      <td>0.593349</td>\n",
       "      <td>0.880606</td>\n",
       "      <td>0.926762</td>\n",
       "      <td>0.400086</td>\n",
       "      <td>0.985533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.743665</td>\n",
       "      <td>0.506964</td>\n",
       "      <td>0.983803</td>\n",
       "      <td>0.992515</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.839009</td>\n",
       "      <td>0.936438</td>\n",
       "      <td>0.603779</td>\n",
       "      <td>0.979620</td>\n",
       "      <td>0.986420</td>\n",
       "      <td>...</td>\n",
       "      <td>0.967561</td>\n",
       "      <td>0.419596</td>\n",
       "      <td>0.992533</td>\n",
       "      <td>0.975581</td>\n",
       "      <td>0.350758</td>\n",
       "      <td>0.590036</td>\n",
       "      <td>0.881465</td>\n",
       "      <td>0.927928</td>\n",
       "      <td>0.400454</td>\n",
       "      <td>0.986226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>0.339346</td>\n",
       "      <td>0.704154</td>\n",
       "      <td>0.592981</td>\n",
       "      <td>0.593349</td>\n",
       "      <td>0.590036</td>\n",
       "      <td>0.591509</td>\n",
       "      <td>0.604025</td>\n",
       "      <td>0.719983</td>\n",
       "      <td>0.582562</td>\n",
       "      <td>0.586549</td>\n",
       "      <td>...</td>\n",
       "      <td>0.578681</td>\n",
       "      <td>0.728695</td>\n",
       "      <td>0.587930</td>\n",
       "      <td>0.595067</td>\n",
       "      <td>0.739616</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.610651</td>\n",
       "      <td>0.571516</td>\n",
       "      <td>0.726732</td>\n",
       "      <td>0.586173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>0.723050</td>\n",
       "      <td>0.486594</td>\n",
       "      <td>0.878766</td>\n",
       "      <td>0.880606</td>\n",
       "      <td>0.881465</td>\n",
       "      <td>0.791398</td>\n",
       "      <td>0.875330</td>\n",
       "      <td>0.606234</td>\n",
       "      <td>0.871177</td>\n",
       "      <td>0.875349</td>\n",
       "      <td>...</td>\n",
       "      <td>0.860859</td>\n",
       "      <td>0.465734</td>\n",
       "      <td>0.879691</td>\n",
       "      <td>0.870544</td>\n",
       "      <td>0.446960</td>\n",
       "      <td>0.610651</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.831751</td>\n",
       "      <td>0.489539</td>\n",
       "      <td>0.875746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>0.704382</td>\n",
       "      <td>0.490779</td>\n",
       "      <td>0.919197</td>\n",
       "      <td>0.926762</td>\n",
       "      <td>0.927928</td>\n",
       "      <td>0.797134</td>\n",
       "      <td>0.877291</td>\n",
       "      <td>0.579437</td>\n",
       "      <td>0.936716</td>\n",
       "      <td>0.931713</td>\n",
       "      <td>...</td>\n",
       "      <td>0.937958</td>\n",
       "      <td>0.416161</td>\n",
       "      <td>0.928899</td>\n",
       "      <td>0.911675</td>\n",
       "      <td>0.356674</td>\n",
       "      <td>0.571516</td>\n",
       "      <td>0.831751</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.402194</td>\n",
       "      <td>0.924192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>0.606234</td>\n",
       "      <td>0.798024</td>\n",
       "      <td>0.407571</td>\n",
       "      <td>0.400086</td>\n",
       "      <td>0.400454</td>\n",
       "      <td>0.517271</td>\n",
       "      <td>0.438493</td>\n",
       "      <td>0.732622</td>\n",
       "      <td>0.398456</td>\n",
       "      <td>0.398326</td>\n",
       "      <td>...</td>\n",
       "      <td>0.397171</td>\n",
       "      <td>0.877048</td>\n",
       "      <td>0.397654</td>\n",
       "      <td>0.413584</td>\n",
       "      <td>0.925271</td>\n",
       "      <td>0.726732</td>\n",
       "      <td>0.489539</td>\n",
       "      <td>0.402194</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.387165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>0.739304</td>\n",
       "      <td>0.504007</td>\n",
       "      <td>0.976645</td>\n",
       "      <td>0.985533</td>\n",
       "      <td>0.986226</td>\n",
       "      <td>0.836643</td>\n",
       "      <td>0.930515</td>\n",
       "      <td>0.600816</td>\n",
       "      <td>0.973658</td>\n",
       "      <td>0.979757</td>\n",
       "      <td>...</td>\n",
       "      <td>0.965084</td>\n",
       "      <td>0.416560</td>\n",
       "      <td>0.986057</td>\n",
       "      <td>0.968725</td>\n",
       "      <td>0.349387</td>\n",
       "      <td>0.586173</td>\n",
       "      <td>0.875746</td>\n",
       "      <td>0.924192</td>\n",
       "      <td>0.387165</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>64 rows × 64 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    4\\5G用户_5G  通话过程中突然中断_-1  其他，请注明.1_98  4\\5G用户_2G  客户星级标识_准星  客户星级标识_银卡  \\\n",
       "0    1.000000      0.628321     0.740720   0.739861   0.743665   0.741702   \n",
       "1    0.628321      1.000000     0.507700   0.507577   0.506964   0.564145   \n",
       "2    0.740720      0.507700     1.000000   0.982944   0.983803   0.836800   \n",
       "3    0.739861      0.507577     0.982944   1.000000   0.992515   0.838395   \n",
       "4    0.743665      0.506964     0.983803   0.992515   1.000000   0.839009   \n",
       "..        ...           ...          ...        ...        ...        ...   \n",
       "59   0.339346      0.704154     0.592981   0.593349   0.590036   0.591509   \n",
       "60   0.723050      0.486594     0.878766   0.880606   0.881465   0.791398   \n",
       "61   0.704382      0.490779     0.919197   0.926762   0.927928   0.797134   \n",
       "62   0.606234      0.798024     0.407571   0.400086   0.400454   0.517271   \n",
       "63   0.739304      0.504007     0.976645   0.985533   0.986226   0.836643   \n",
       "\n",
       "        农村_6  客户星级标识_三星   未接通掉话次数      脱网次数  ...  语音通话-时长（分钟）     高铁_-1  \\\n",
       "0   0.730167   0.615437  0.736468  0.738419  ...     0.729502  0.604516   \n",
       "1   0.496165   0.717161  0.501141  0.504282  ...     0.498788  0.844162   \n",
       "2   0.930793   0.605252  0.969890  0.976648  ...     0.958225  0.426959   \n",
       "3   0.935088   0.607583  0.978635  0.985538  ...     0.966709  0.420701   \n",
       "4   0.936438   0.603779  0.979620  0.986420  ...     0.967561  0.419596   \n",
       "..       ...        ...       ...       ...  ...          ...       ...   \n",
       "59  0.604025   0.719983  0.582562  0.586549  ...     0.578681  0.728695   \n",
       "60  0.875330   0.606234  0.871177  0.875349  ...     0.860859  0.465734   \n",
       "61  0.877291   0.579437  0.936716  0.931713  ...     0.937958  0.416161   \n",
       "62  0.438493   0.732622  0.398456  0.398326  ...     0.397171  0.877048   \n",
       "63  0.930515   0.600816  0.973658  0.979757  ...     0.965084  0.416560   \n",
       "\n",
       "    套外流量（MB）  客户星级标识_一星     高校_-1  是否5G网络客户_否  有信号无法拨通_2   mos质差次数  \\\n",
       "0   0.742592   0.738143  0.593840    0.339346   0.723050  0.704382   \n",
       "1   0.505397   0.515430  0.831769    0.704154   0.486594  0.490779   \n",
       "2   0.982606   0.966501  0.358366    0.592981   0.878766  0.919197   \n",
       "3   0.991609   0.974968  0.351617    0.593349   0.880606  0.926762   \n",
       "4   0.992533   0.975581  0.350758    0.590036   0.881465  0.927928   \n",
       "..       ...        ...       ...         ...        ...       ...   \n",
       "59  0.587930   0.595067  0.739616    1.000000   0.610651  0.571516   \n",
       "60  0.879691   0.870544  0.446960    0.610651   1.000000  0.831751   \n",
       "61  0.928899   0.911675  0.356674    0.571516   0.831751  1.000000   \n",
       "62  0.397654   0.413584  0.925271    0.726732   0.489539  0.402194   \n",
       "63  0.986057   0.968725  0.349387    0.586173   0.875746  0.924192   \n",
       "\n",
       "    外省语音占比_0.0  省际漫游-时长（分钟）  \n",
       "0     0.606234     0.739304  \n",
       "1     0.798024     0.504007  \n",
       "2     0.407571     0.976645  \n",
       "3     0.400086     0.985533  \n",
       "4     0.400454     0.986226  \n",
       "..         ...          ...  \n",
       "59    0.726732     0.586173  \n",
       "60    0.489539     0.875746  \n",
       "61    0.402194     0.924192  \n",
       "62    1.000000     0.387165  \n",
       "63    0.387165     1.000000  \n",
       "\n",
       "[64 rows x 64 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "play_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "3ba090df-9777-4bf1-8282-ac7387b9fd31",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "def ShowGRAHeatMap(DataFrame):\n",
    "    colormap = plt.cm.hsv\n",
    "    ylabels = DataFrame.columns.values.tolist()\n",
    "    f, ax = plt.subplots(figsize=(15, 15))\n",
    "    ax.set_title('Wine GRA')\n",
    "    # 设置展示一半，如果不需要注释掉mask即可\n",
    "    mask = np.zeros_like(DataFrame)\n",
    "    mask[np.triu_indices_from(mask)] = True  # np.triu_indices 上三角矩阵\n",
    " \n",
    "    with sns.axes_style(\"white\"):\n",
    "        sns.heatmap(DataFrame,\n",
    "                    cmap=\"YlGnBu\",\n",
    "                    mask=mask,\n",
    "                    )\n",
    "    plt.savefig('./ans2/灰色关联热力图.jpg')\n",
    "    plt.show()\n",
    "ShowGRAHeatMap(play_score)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3cf045bf-5fa4-4767-934c-9603f5c8fa5a",
   "metadata": {},
   "source": [
    "统计出每个特征关联度的均值后，我们发现大部分的特征关联度都在0.4与0.9之间，也就是说大部分特征都与结果呈现出了相对较高的关联性。\n",
    "这也意味着已有的数据源的特征关联度对之前模型的影响是有限的。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "e4a0f85c-e303-4b90-9507-40bd6e62756b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple\n",
      "Collecting hyperopt\n",
      "  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/b6/cd/5b3334d39276067f54618ce0d0b48ed69d91352fbf137468c7095170d0e5/hyperopt-0.2.7-py2.py3-none-any.whl (1.6 MB)\n",
      "     ---------------------------------------- 1.6/1.6 MB 2.3 MB/s eta 0:00:00\n",
      "Collecting cloudpickle\n",
      "  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/cf/26/cd6c4177273ee35f7a31245893489c68bc340988f12ca315b392f1f18a93/cloudpickle-2.2.0-py3-none-any.whl (25 kB)\n",
      "Requirement already satisfied: six in c:\\users\\linling\\appdata\\roaming\\spsspro\\spsspro\\lib\\python\\lib\\site-packages (from hyperopt) (1.16.0)\n",
      "Requirement already satisfied: scipy in c:\\users\\linling\\appdata\\roaming\\spsspro\\spsspro\\lib\\python\\lib\\site-packages (from hyperopt) (1.9.3)\n",
      "Requirement already satisfied: networkx>=2.2 in c:\\users\\linling\\appdata\\roaming\\spsspro\\spsspro\\lib\\python\\lib\\site-packages (from hyperopt) (2.8.8)\n",
      "Collecting tqdm\n",
      "  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/47/bb/849011636c4da2e44f1253cd927cfb20ada4374d8b3a4e425416e84900cc/tqdm-4.64.1-py2.py3-none-any.whl (78 kB)\n",
      "     ---------------------------------------- 78.5/78.5 kB 4.5 MB/s eta 0:00:00\n",
      "Collecting py4j\n",
      "  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/10/30/a58b32568f1623aaad7db22aa9eafc4c6c194b429ff35bdc55ca2726da47/py4j-0.10.9.7-py2.py3-none-any.whl (200 kB)\n",
      "     ---------------------------------------- 200.5/200.5 kB ? eta 0:00:00\n",
      "Requirement already satisfied: future in c:\\users\\linling\\appdata\\roaming\\spsspro\\spsspro\\lib\\python\\lib\\site-packages (from hyperopt) (0.18.2)\n",
      "Requirement already satisfied: numpy in c:\\users\\linling\\appdata\\roaming\\spsspro\\spsspro\\lib\\python\\lib\\site-packages (from hyperopt) (1.22.3)\n",
      "Requirement already satisfied: colorama in c:\\users\\linling\\appdata\\roaming\\spsspro\\spsspro\\lib\\python\\lib\\site-packages (from tqdm->hyperopt) (0.4.4)\n",
      "Installing collected packages: py4j, tqdm, cloudpickle, hyperopt\n",
      "Successfully installed cloudpickle-2.2.0 hyperopt-0.2.7 py4j-0.10.9.7 tqdm-4.64.1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  WARNING: The script tqdm.exe is installed in 'C:\\Users\\Linling\\AppData\\Roaming\\spsspro\\spsspro\\lib\\python\\Scripts' which is not on PATH.\n",
      "  Consider adding this directory to PATH or, if you prefer to suppress this warning, use --no-warn-script-location.\n",
      "  WARNING: The script hyperopt-mongo-worker.exe is installed in 'C:\\Users\\Linling\\AppData\\Roaming\\spsspro\\spsspro\\lib\\python\\Scripts' which is not on PATH.\n",
      "  Consider adding this directory to PATH or, if you prefer to suppress this warning, use --no-warn-script-location.\n"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "!{sys.executable} -m pip install hyperopt -i https://pypi.tuna.tsinghua.edu.cn/simple"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "f3ad0b81-1fac-4cb6-94cf-e729bbf20da8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100%|██████████| 100/100 [01:06<00:00,  1.50trial/s, best loss: -0.5840490797546012]\n"
     ]
    }
   ],
   "source": [
    "from hyperopt import fmin, tpe, hp, STATUS_OK, Trials\n",
    "#基于TPE算法进行全局寻优\n",
    "\n",
    "a=[]\n",
    "b=[]\n",
    "def hyperopt_train_test(params): \n",
    "    clf = RandomForestClassifier(**params,random_state=0)\n",
    "    clf.fit(tr_x,tr_y)\n",
    "    y_pred = clf.predict(te_x)\n",
    "    pj=f1_score(te_y,y_pred,average=\"micro\")\n",
    "    a.append(pj)\n",
    "    b.append(params)\n",
    "    return pj\n",
    " \n",
    "space = {\n",
    "    'n_estimators': hp.choice('n_estimators', range(1,500,5)),\n",
    "    'max_depth': hp.choice('learning_rate',  range(1,50)),\n",
    "    'min_samples_split': hp.choice('min_samples_split', range(1,50)),\n",
    "    'min_samples_leaf': hp.choice('min_samples_leaf', range(1,20))}\n",
    " \n",
    "def f(params):\n",
    "    acc = hyperopt_train_test(params)\n",
    "    return {'loss': -acc, 'status': STATUS_OK} #注意这里的负号\n",
    " \n",
    "trials = Trials()\n",
    "best = fmin(f, space, algo=tpe.suggest, max_evals=100, trials=trials)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "f8d151a2-ff34-4b12-8abf-7406edabc446",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'max_depth': 23, 'min_samples_leaf': 8, 'min_samples_split': 17, 'n_estimators': 106}\n"
     ]
    }
   ],
   "source": [
    "max_f1_dict=b[a.index(max(a))]\n",
    "print(max_f1_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "a9f059c0-f4d4-4376-9de0-06133fd0a92c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "随机森林:\n",
      "训练集准确度:0.634\n",
      "测试集准确度:0.579\n",
      "平均绝对误差: 1.2368098159509202\n",
      "ACC 0.5785276073619632\n",
      "REC 0.5785276073619632\n",
      "F-score 0.5785276073619632\n"
     ]
    }
   ],
   "source": [
    "print(\"\\n随机森林:\")\n",
    "rf=RandomForestClassifier(**max_f1_dict,random_state=0)\n",
    "rf.fit(tr_x,tr_y)\n",
    "print(\"训练集准确度:{:.3f}\".format(rf.score(tr_x,tr_y)))\n",
    "print(\"测试集准确度:{:.3f}\".format(rf.score(te_x,te_y)))\n",
    "y_pred = rf.predict(te_x)\n",
    "print(\"平均绝对误差:\",mean_absolute_error(te_y, y_pred))\n",
    "print(\"ACC\",accuracy_score(te_y,y_pred))\n",
    "print(\"REC\",recall_score(te_y,y_pred,average=\"micro\"))\n",
    "print(\"F-score\",f1_score(te_y,y_pred,average=\"micro\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "caa77ed7-fc29-46c8-b3c1-033e86a63e59",
   "metadata": {},
   "outputs": [],
   "source": [
    "rf=RandomForestClassifier(**max_f1_dict)\n",
    "rf.fit(X,YY)\n",
    "y_pred = rf.predict(test_X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "e9a0c03c-c099-44a1-b8cb-1d9141919f82",
   "metadata": {},
   "outputs": [
    {
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       "<p>2599 rows × 5 columns</p>\n",
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      ],
      "text/plain": [
       "      用户id  语音通话整体满意度  网络覆盖与信号强度  语音通话清晰度  语音通话稳定性\n",
       "0        1        NaN        NaN      NaN      NaN\n",
       "1        2        NaN        NaN      NaN      NaN\n",
       "2        3        NaN        NaN      NaN      NaN\n",
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       "\n",
       "[2599 rows x 5 columns]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
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    }
   ],
   "source": [
    "result=pd.read_excel('./ans2/result.xlsx',sheet_name='语音')\n",
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "8b35a63b-fe55-4827-b25b-93529c7d28dd",
   "metadata": {},
   "outputs": [],
   "source": [
    "result['语音通话整体满意度']=y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "015c16ab-e69a-495e-b551-4b749f3b4445",
   "metadata": {},
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      "text/plain": [
       "      用户id  语音通话整体满意度  网络覆盖与信号强度  语音通话清晰度  语音通话稳定性\n",
       "0        1         10        NaN      NaN      NaN\n",
       "1        2         10        NaN      NaN      NaN\n",
       "2        3         10        NaN      NaN      NaN\n",
       "3        4         10        NaN      NaN      NaN\n",
       "4        5         10        NaN      NaN      NaN\n",
       "...    ...        ...        ...      ...      ...\n",
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       "2597  2598         10        NaN      NaN      NaN\n",
       "2598  2599         10        NaN      NaN      NaN\n",
       "\n",
       "[2599 rows x 5 columns]"
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  {
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   "metadata": {},
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           1       1.00      1.00      1.00       237\n",
      "           2       1.00      1.00      1.00        62\n",
      "           3       1.00      1.00      1.00        90\n",
      "           4       1.00      1.00      1.00        90\n",
      "           5       1.00      1.00      1.00       211\n",
      "           6       1.00      1.00      1.00       271\n",
      "           7       1.00      1.00      1.00       327\n",
      "           8       1.00      1.00      1.00       658\n",
      "           9       1.00      1.00      1.00       786\n",
      "          10       1.00      1.00      1.00      2701\n",
      "\n",
      "    accuracy                           1.00      5433\n",
      "   macro avg       1.00      1.00      1.00      5433\n",
      "weighted avg       1.00      1.00      1.00      5433\n",
      "\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "随机森林:\n",
      "训练集准确度:0.632\n",
      "测试集准确度:0.501\n",
      "平均绝对误差: 1.4705521472392638\n",
      "ACC 0.5012269938650307\n",
      "REC 0.5012269938650307\n",
      "F-score 0.5012269938650307\n"
     ]
    }
   ],
   "source": [
    "YY=Y2\n",
    "DT=DecisionTreeClassifier( criterion='entropy')\n",
    "\n",
    "see1=DT.fit(X,YY)\n",
    "\n",
    "y_pred=DT.predict(X)\n",
    "\n",
    "print(classification_report(y_pred,YY))\n",
    "\n",
    "\n",
    "# 重要性\n",
    "features_import = pd.DataFrame(X.columns, columns=['feature'])\n",
    "features_import['importance'] = DT.feature_importances_  # 默认按照gini计算特征重要性\n",
    "allcol=dinglei+dingliang\n",
    "def getcol(x):\n",
    "    for i in allcol:\n",
    "        if i in x:\n",
    "            return i\n",
    "\n",
    "features_import['feature']=features_import['feature'].apply(lambda x:getcol(x))\n",
    "features_import=features_import.groupby('feature').sum().reset_index(drop=False)\n",
    "features_import.sort_values('importance', inplace=True)\n",
    "# 绘图\n",
    "from matplotlib import pyplot as plt\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']  # 显示中文黑体\n",
    "# plt.rcParams['axes.unicode_minus'] = False # 负值显示\n",
    "fig, ax = plt.subplots(figsize=(15, 10))\n",
    "\n",
    "plt.barh(features_import['feature'], features_import['importance'], height=0.7, color='#008792', edgecolor='#005344') # 更多颜色可参见颜色大全\n",
    "plt.xlabel('feature importance') # x 轴\n",
    "plt.ylabel('features') # y轴\n",
    "plt.title('网络覆盖与信号强度 Feature Importances') # 标题\n",
    "for a,b in zip( features_import['importance'],features_import['feature']): # 添加数字标签\n",
    "    plt.text(a+0.001, b,'%.3f'%float(a)) # a+0.001代表标签位置在柱形图上方0.001处\n",
    "plt.savefig('./ans2/网络覆盖与信号强度featureimportance')\n",
    "plt.show()\n",
    "\n",
    "tr_x,te_x,tr_y,te_y=train_test_split(X,YY,test_size=0.3,random_state=5)\n",
    "\n",
    "print(\"\\n随机森林:\")\n",
    "rf=RandomForestClassifier(**max_f1_dict)\n",
    "rf.fit(tr_x,tr_y)\n",
    "print(\"训练集准确度:{:.3f}\".format(rf.score(tr_x,tr_y)))\n",
    "print(\"测试集准确度:{:.3f}\".format(rf.score(te_x,te_y)))\n",
    "y_pred = rf.predict(te_x)\n",
    "print(\"平均绝对误差:\",mean_absolute_error(te_y, y_pred))\n",
    "print(\"ACC\",accuracy_score(te_y,y_pred))\n",
    "print(\"REC\",recall_score(te_y,y_pred,average=\"micro\"))\n",
    "print(\"F-score\",f1_score(te_y,y_pred,average=\"micro\"))\n",
    "\n",
    "\n",
    "rf=RandomForestClassifier(**max_f1_dict)\n",
    "rf.fit(X,YY)\n",
    "y_pred = rf.predict(test_X)\n",
    "result['网络覆盖与信号强度']=y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d2ca52fc-e964-4159-8582-d21698092346",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "0561d5cb-646d-40b2-82ed-d58cea0efbe4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           1       1.00      1.00      1.00       184\n",
      "           2       1.00      1.00      1.00        40\n",
      "           3       1.00      1.00      1.00        59\n",
      "           4       1.00      1.00      1.00        85\n",
      "           5       1.00      1.00      1.00       161\n",
      "           6       1.00      1.00      1.00       207\n",
      "           7       1.00      1.00      1.00       268\n",
      "           8       1.00      1.00      1.00       643\n",
      "           9       1.00      1.00      1.00       805\n",
      "          10       1.00      1.00      1.00      2981\n",
      "\n",
      "    accuracy                           1.00      5433\n",
      "   macro avg       1.00      1.00      1.00      5433\n",
      "weighted avg       1.00      1.00      1.00      5433\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "随机森林:\n",
      "训练集准确度:0.634\n",
      "测试集准确度:0.555\n",
      "平均绝对误差: 1.2361963190184049\n",
      "ACC 0.5552147239263804\n",
      "REC 0.5552147239263804\n",
      "F-score 0.5552147239263804\n"
     ]
    }
   ],
   "source": [
    "YY=Y3\n",
    "DT=DecisionTreeClassifier( criterion='entropy')\n",
    "\n",
    "see1=DT.fit(X,YY)\n",
    "\n",
    "y_pred=DT.predict(X)\n",
    "\n",
    "print(classification_report(y_pred,YY))\n",
    "\n",
    "# 重要性\n",
    "features_import = pd.DataFrame(X.columns, columns=['feature'])\n",
    "features_import['importance'] = DT.feature_importances_  # 默认按照gini计算特征重要性\n",
    "allcol=dinglei+dingliang\n",
    "def getcol(x):\n",
    "    for i in allcol:\n",
    "        if i in x:\n",
    "            return i\n",
    "\n",
    "features_import['feature']=features_import['feature'].apply(lambda x:getcol(x))\n",
    "features_import=features_import.groupby('feature').sum().reset_index(drop=False)\n",
    "features_import.sort_values('importance', inplace=True)\n",
    "# 绘图\n",
    "from matplotlib import pyplot as plt\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']  # 显示中文黑体\n",
    "# plt.rcParams['axes.unicode_minus'] = False # 负值显示\n",
    "fig, ax = plt.subplots(figsize=(15, 10))\n",
    "\n",
    "plt.barh(features_import['feature'], features_import['importance'], height=0.7, color='#008792', edgecolor='#005344') # 更多颜色可参见颜色大全\n",
    "plt.xlabel('feature importance') # x 轴\n",
    "plt.ylabel('features') # y轴\n",
    "plt.title('语音通话清晰度 Feature Importances') # 标题\n",
    "for a,b in zip( features_import['importance'],features_import['feature']): # 添加数字标签\n",
    "    plt.text(a+0.001, b,'%.3f'%float(a)) # a+0.001代表标签位置在柱形图上方0.001处\n",
    "plt.savefig('./ans2/语音通话清晰度featureimportance')\n",
    "plt.show()\n",
    "\n",
    "\n",
    "tr_x,te_x,tr_y,te_y=train_test_split(X,YY,test_size=0.3,random_state=5)\n",
    "\n",
    "print(\"\\n随机森林:\")\n",
    "rf=RandomForestClassifier(**max_f1_dict)\n",
    "rf.fit(tr_x,tr_y)\n",
    "print(\"训练集准确度:{:.3f}\".format(rf.score(tr_x,tr_y)))\n",
    "print(\"测试集准确度:{:.3f}\".format(rf.score(te_x,te_y)))\n",
    "y_pred = rf.predict(te_x)\n",
    "print(\"平均绝对误差:\",mean_absolute_error(te_y, y_pred))\n",
    "print(\"ACC\",accuracy_score(te_y,y_pred))\n",
    "print(\"REC\",recall_score(te_y,y_pred,average=\"micro\"))\n",
    "print(\"F-score\",f1_score(te_y,y_pred,average=\"micro\"))\n",
    "\n",
    "\n",
    "rf=RandomForestClassifier(**max_f1_dict)\n",
    "rf.fit(X,YY)\n",
    "y_pred = rf.predict(test_X)\n",
    "result['语音通话清晰度']=y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "766dcb8b-051e-4c4a-9ef0-a9ec355e69fe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           1       1.00      1.00      1.00       230\n",
      "           2       1.00      1.00      1.00        49\n",
      "           3       1.00      1.00      1.00        83\n",
      "           4       1.00      1.00      1.00        93\n",
      "           5       1.00      1.00      1.00       201\n",
      "           6       1.00      1.00      1.00       228\n",
      "           7       1.00      1.00      1.00       309\n",
      "           8       1.00      1.00      1.00       654\n",
      "           9       1.00      1.00      1.00       786\n",
      "          10       1.00      1.00      1.00      2800\n",
      "\n",
      "    accuracy                           1.00      5433\n",
      "   macro avg       1.00      1.00      1.00      5433\n",
      "weighted avg       1.00      1.00      1.00      5433\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "随机森林:\n",
      "训练集准确度:0.627\n",
      "测试集准确度:0.520\n",
      "平均绝对误差: 1.3791411042944786\n",
      "ACC 0.5196319018404908\n",
      "REC 0.5196319018404908\n",
      "F-score 0.5196319018404908\n"
     ]
    }
   ],
   "source": [
    "YY=Y4\n",
    "DT=DecisionTreeClassifier( criterion='entropy')\n",
    "\n",
    "see1=DT.fit(X,YY)\n",
    "\n",
    "y_pred=DT.predict(X)\n",
    "\n",
    "print(classification_report(y_pred,YY))\n",
    "\n",
    "\n",
    "\n",
    "# 重要性\n",
    "features_import = pd.DataFrame(X.columns, columns=['feature'])\n",
    "features_import['importance'] = DT.feature_importances_  # 默认按照gini计算特征重要性\n",
    "allcol=dinglei+dingliang\n",
    "def getcol(x):\n",
    "    for i in allcol:\n",
    "        if i in x:\n",
    "            return i\n",
    "\n",
    "features_import['feature']=features_import['feature'].apply(lambda x:getcol(x))\n",
    "features_import=features_import.groupby('feature').sum().reset_index(drop=False)\n",
    "features_import.sort_values('importance', inplace=True)\n",
    "# 绘图\n",
    "from matplotlib import pyplot as plt\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']  # 显示中文黑体\n",
    "# plt.rcParams['axes.unicode_minus'] = False # 负值显示\n",
    "fig, ax = plt.subplots(figsize=(15, 10))\n",
    "\n",
    "plt.barh(features_import['feature'], features_import['importance'], height=0.7, color='#008792', edgecolor='#005344') # 更多颜色可参见颜色大全\n",
    "plt.xlabel('feature importance') # x 轴\n",
    "plt.ylabel('features') # y轴\n",
    "plt.title('语音通话稳定性 Feature Importances') # 标题\n",
    "for a,b in zip( features_import['importance'],features_import['feature']): # 添加数字标签\n",
    "    plt.text(a+0.001, b,'%.3f'%float(a)) # a+0.001代表标签位置在柱形图上方0.001处\n",
    "plt.savefig('./ans2/语音通话稳定性featureimportance')\n",
    "plt.show()\n",
    "\n",
    "tr_x,te_x,tr_y,te_y=train_test_split(X,YY,test_size=0.3,random_state=5)\n",
    "\n",
    "print(\"\\n随机森林:\")\n",
    "rf=RandomForestClassifier(**max_f1_dict)\n",
    "rf.fit(tr_x,tr_y)\n",
    "print(\"训练集准确度:{:.3f}\".format(rf.score(tr_x,tr_y)))\n",
    "print(\"测试集准确度:{:.3f}\".format(rf.score(te_x,te_y)))\n",
    "y_pred = rf.predict(te_x)\n",
    "print(\"平均绝对误差:\",mean_absolute_error(te_y, y_pred))\n",
    "print(\"ACC\",accuracy_score(te_y,y_pred))\n",
    "print(\"REC\",recall_score(te_y,y_pred,average=\"micro\"))\n",
    "print(\"F-score\",f1_score(te_y,y_pred,average=\"micro\"))\n",
    "\n",
    "\n",
    "rf=RandomForestClassifier(**max_f1_dict)\n",
    "rf.fit(X,YY)\n",
    "y_pred = rf.predict(test_X)\n",
    "result['语音通话稳定性']=y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "40960f89-7d6b-4c64-b054-5d5ba2fab0d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "result.to_excel('result_语音.xlsx',index=None)"
   ]
  }
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